RESEARCH ON SURROGATE MODELS FOR FATIGUE LOADS PREDICTION OF WIND TURBINES BASED ON DEEP NEURAL NETWORK

Huang Guoqing; Liu Weijie; Wang Binbin; Peng Liuliu; Yang Qingshan; Tan Shu

doi:10.19912/j.0254-0096.tynxb.2023-1999

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 398-405. DOI: 10.19912/j.0254-0096.tynxb.2023-1999

RESEARCH ON SURROGATE MODELS FOR FATIGUE LOADS PREDICTION OF WIND TURBINES BASED ON DEEP NEURAL NETWORK

Huang Guoqing¹, Liu Weijie¹, Wang Binbin², Peng Liuliu¹, Yang Qingshan¹, Tan Shu¹

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Abstract

A deep neural network （DNN）-based surrogate model for wind turbine fatigue load is proposed to address the low-efficiency of site-specific suitability assessment. A surrogate model of fatigue loads of wind turbines based on deep neural network （DNN） is comprehensively investigated. Firstly, 10000 samples of six environmental variable space including average wind speed, turbulence intensity, wind shear, yaw misalignment, vertical inflow angle and air density are generated by quasi-Monte Carlo method. Then, TurbSim and OpenFAST are used to simulate the load time history of the NREL 5MW reference wind turbine, and MLife is used to obtain the damage equivalent load （DEL） database at 1 Hz. Finally, the DNN method is used to establish DEL surrogate models for 7 load channels, and the accuracy of the models are extensively verified. Results show that the DNN-based DEL surrogate models have high prediction accuracy and computational efficiency.

Key words

wind turbine / fatigue load / OpenFAST / deep neural network / surrogate model

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Huang Guoqing, Liu Weijie, Wang Binbin, Peng Liuliu, Yang Qingshan, Tan Shu. RESEARCH ON SURROGATE MODELS FOR FATIGUE LOADS PREDICTION OF WIND TURBINES BASED ON DEEP NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 398-405 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1999

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